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Background@#Results of studies investigating the association between body mass index (BMI) and mortality in patients with coronavirus disease-2019 (COVID-19) have been conflicting. @*Methods@#This multicenter, retrospective observational study, conducted between January 2020 and August 2021, evaluated the impact of obesity on outcomes in patients with severe COVID-19 in a Korean national cohort. A total of 1,114 patients were enrolled from 22 tertiary referral hospitals or university-affiliated hospitals, of whom 1,099 were included in the analysis, excluding 15 with unavailable height and weight information. The effect(s) of BMI on patients with severe COVID-19 were analyzed. @*Results@#According to the World Health Organization BMI classification, 59 patients were underweight, 541 were normal, 389 were overweight, and 110 were obese. The overall 28-day mortality rate was 15.3%, and there was no significant difference according to BMI. Univariate Cox analysis revealed that BMI was associated with 28-day mortality (hazard ratio, 0.96; p=0.045), but not in the multivariate analysis. Additionally, patients were divided into two groups based on BMI ≥25 kg/m2 and underwent propensity score matching analysis, in which the two groups exhibited no significant difference in mortality at 28 days. The median (interquartile range) clinical frailty scale score at discharge was higher in nonobese patients (3 [3 to 5] vs. 4 [3 to 6], p<0.001). The proportion of frail patients at discharge was significantly higher in the nonobese group (28.1% vs. 46.8%, p<0.001). @*Conclusion@#The obesity paradox was not evident in this cohort of patients with severe COVID-19. However, functional outcomes at discharge were better in the obese group.
RESUMEN
In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's T² test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.